The purpose of this article is to find the settings of the factors which simultaneously optimize several mean responses in a multivariate generalized linear model (GLM) environment. The generalized distance approach, initially developed for the simultaneous optimization of several linear response surface models, is adapted to this multivariate GLM situation. An application of the proposed methodology is presented in the special case of a bivariate binary distribution resulting from a drug testing experiment concerning two responses, namely, the efficacy and toxicity of a particular drug combination. One of the objectives of this application is to find the dose levels of two drugs that simultaneously maximize their therapeutic effect and min...
AbstractWe introduce a class of multivariate dispersion models suitable as error distributions for g...
In many complex experiments, nuisance factor may have large effects that must be accounted for. Cova...
Optimal design theory deals with the assessment of the optimal joint distribution of all independent...
A simple heuristic is proposed for constructing robust experimental designs for multivariate general...
A Bivariate Optimizing Up-and-Down design for selecting the drug combination with maximum success pr...
abstract: Bivariate responses that comprise mixtures of binary and continuous variables are common i...
Dose optimization studies of new therapeutic agents aim to identify one or more promising doses for ...
We propose a Multivariate Logistic Distance (MLD) model for the analysis of multiple binary response...
Response surface methodology involves relationships between different variables, specifically experi...
We consider a bivariate logistic model for a binary response, and we assume that two rival dependenc...
Multinomial responses frequently occur in dose level experiments. For example, in a study of the inf...
Generalized linear mixed models (GLMMs) are commonly used for analyzing clustered correlated discret...
In this dissertation we developed statistical tools for analyzing multivariate binary data. In many ...
We introduce a class of multivariate dispersion models suitable as error distributions for generaliz...
This paper introduces local distance-based generalized linear models. These models extend (weighted)...
AbstractWe introduce a class of multivariate dispersion models suitable as error distributions for g...
In many complex experiments, nuisance factor may have large effects that must be accounted for. Cova...
Optimal design theory deals with the assessment of the optimal joint distribution of all independent...
A simple heuristic is proposed for constructing robust experimental designs for multivariate general...
A Bivariate Optimizing Up-and-Down design for selecting the drug combination with maximum success pr...
abstract: Bivariate responses that comprise mixtures of binary and continuous variables are common i...
Dose optimization studies of new therapeutic agents aim to identify one or more promising doses for ...
We propose a Multivariate Logistic Distance (MLD) model for the analysis of multiple binary response...
Response surface methodology involves relationships between different variables, specifically experi...
We consider a bivariate logistic model for a binary response, and we assume that two rival dependenc...
Multinomial responses frequently occur in dose level experiments. For example, in a study of the inf...
Generalized linear mixed models (GLMMs) are commonly used for analyzing clustered correlated discret...
In this dissertation we developed statistical tools for analyzing multivariate binary data. In many ...
We introduce a class of multivariate dispersion models suitable as error distributions for generaliz...
This paper introduces local distance-based generalized linear models. These models extend (weighted)...
AbstractWe introduce a class of multivariate dispersion models suitable as error distributions for g...
In many complex experiments, nuisance factor may have large effects that must be accounted for. Cova...
Optimal design theory deals with the assessment of the optimal joint distribution of all independent...